pyPDAF.PDAF.diag_crps_mpi

pyPDAF.PDAF.diag_crps_mpi()

Obtain a continuous rank probability score for an ensemble.

The implementation is based on [1].

References

[1]

Hersbach, H. (2000), Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Wea. Forecasting, 15, 559–570, doi:10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2

Parameters:

• dim_p (int) – Dimension of state vector

• dim_ens (int) – Ensemble size

• element (int) – ID of element to be used. If element=0, mean values over all elements are computed

• oens (ndarray[tuple[dim, dim_ens, ...], np.float64]) – State ensemble. shape: (dim_p, dim_ens)

• obs (ndarray[tuple[dim, ...], np.float64]) – State ensemble. shape: (dim_p)

• comm_filter (int) – MPI communicator for filter

• mype_filter (int) – rank of MPI communicator

• npes_filter (int) – size of MPI communicator

Returns:

• crps (double) – CRPS

• reli (double) – Reliability

• pot_crps (double) – potential CRPS

• uncert (double) – uncertainty

• status (int) – Status flag (0=success)